66f878cee9
* Remove dependency: Standard --> MemRef * Add dependencies: GPUToNVVMTransforms --> MemRef, Linalg --> MemRef, MemRef --> Tensor * Note: The `subtensor_insert_propagate_dest_cast` test case in MemRef/canonicalize.mlir will be moved to Tensor/canonicalize.mlir in a subsequent commit, which moves over the remaining Tensor ops from the Standard dialect to the Tensor dialect. Differential Revision: https://reviews.llvm.org/D104506
40 lines
1.3 KiB
C++
40 lines
1.3 KiB
C++
//====----- Bufferize.cpp - Bufferization of shape ops ---------*- C++-*--===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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#include "mlir/Transforms/Bufferize.h"
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#include "PassDetail.h"
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#include "mlir/Dialect/MemRef/IR/MemRef.h"
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#include "mlir/Dialect/Shape/Transforms/Passes.h"
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#include "mlir/Pass/Pass.h"
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using namespace mlir;
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namespace {
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struct ShapeBufferizePass : public ShapeBufferizeBase<ShapeBufferizePass> {
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void runOnFunction() override {
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MLIRContext &ctx = getContext();
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RewritePatternSet patterns(&ctx);
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BufferizeTypeConverter typeConverter;
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ConversionTarget target(ctx);
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populateBufferizeMaterializationLegality(target);
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populateShapeStructuralTypeConversionsAndLegality(typeConverter, patterns,
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target);
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if (failed(
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applyPartialConversion(getFunction(), target, std::move(patterns))))
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signalPassFailure();
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}
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};
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} // namespace
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std::unique_ptr<FunctionPass> mlir::createShapeBufferizePass() {
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return std::make_unique<ShapeBufferizePass>();
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}
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